High-fidelity quantum chemical (QM) data sets that jointly resolve reaction thermochemistry, kinetics, and solvation at scale remain scarce, especially for radical chemistry. We introduce QuantumPioneer, an open-access reaction-centered QM database and workflow for small organic molecules, focused on peroxyl-mediated hydrogen atom transfer (HAT) and the corresponding homolytic bond dissociation reactions. QuantumPioneer contains 348,258 species (2–21 heavy atoms), 167,237 validated HAT transition states (TS) with corresponding reaction energies and homolytic bond dissociation energies (BDEs), and over 100 million COSMO-RS solvation free energies(ΔGsolv*) and enthalpies (ΔHsolv*) across 295 solvents. The workflow uses ωB97X-D/def2-SVP geometries, DLPNO–CCSD(T)-F12d/cc-pVTZ-F12 single-point energies, empirical thermochemical corrections, transition-state theory, and COSMO-RS BP-TZVPD-FINE solvation in a single high-throughput pipeline. Our benchmarks show reliable accuracy, with mean absolute errors (MAEs) compared to experimental and high-level QM reference data of 0.82 kcal/mol for gas-phase enthalpies of formation, 1.60 kcal/mol for C–H BDEs, 1.45 kcal/mol for HAT barriers, and 0.57 kcal/mol for ΔGsolv* values. We demonstrate two predictive applications. First, we show that combining BDE and HAT-barrier models identifies experimentally observed oxidative degradation sites in drug-like molecules with a 91% top-5 hit rate and 82% site-level recall. Second, we show that a QM-parametrized Abraham model enables rapid solvation energy estimates at near-COSMO-RS accuracy within its training domain, reproducing computed ΔGsolv* and ΔHsolv* values with MAEs of 0.16 and 0.18 kcal/mol, respectively, though performance on experimental ΔGsolv*values for unseen solutes was worse, with an MAE of 1.32 kcal/mol. This work provides a scalable template for other reaction families, unifying equilibrium species, validated TS, thermochemistry, kinetics, and solvation into one workflow.
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This work presents a model validation framework consisting of five recommendations that would enable the community to move beyond aggregate metrics toward understanding where and why molecular property prediction models fail, and connects evaluation choices to real-world applications and case studies encountered in pharmaceutical research.
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